The Betwa River basin, a semi-arid catchment that has been classified as a major hotspot of groundwater depletion in Central India. The rainfall and streamflow intermittency have affected agricultural practices due to the variability of groundwater availability for irrigation. This study evaluates the spatial and temporal variations of groundwater level (GWL) in the last 25 years (1993–2018) in the catchment. We applied a nonparametric Seasonal Trend decomposition based on the Loess (STL) method to decompose the GWL time series into the seasonal, trend, and remainder components. We observed that the GWL in the northeastern regions of the basin has depleted about 3–5 mbgl in the last two decades. During the same period, the basin has experienced a reduction in the rainfall magnitude (2.07 mm/yr). We observed that the overexploitation of groundwater for irrigation and rainfall variability have greatly impacted the GWL condition in the study area. Further, if the groundwater extraction continues at present rates, the Betwa River basin may experience severe depletion in the future.

We estimate the combined effect of climate and landuse-landcover (LU-LC) change on the streamflow of the Betwa River; a semi-arid catchment in Central India. We have used the observed and future bias-corrected climatic datasets from 1980–2100. To assess the LU-LC change in the catchment, we have processed and classified the Landsat satellite images from 1990–2020. We have used Artificial Neural Network (ANN) based Cellular Automata (CA) model to simulate the future LU-LC. Further, we coupled the observed and projected LU-LC and climatic variables in the SWAT (Soil and water assessment tool) model to simulate the streamflow of the Betwa River. In doing so, we have setup this model for the observed (1980–2000 and 2001–2020) and projected (2023–2060 and 2061–2100) time periods by using the LU-LC of the years 1990, 2018, and 2040, 2070, respectively. We observed that the combined effect of climate and LU-LC change resulted in the reduction in the mean monsoon stream flow of the Betwa River by 16% during 2001–2020 as compared to 1982–2000. In all four CMIP6 climatic scenarios (SSP126, SSP245, SSP370, and SSP585), the mean monsoon stream flow is expected to decrease by 39–47% and 31–47% during 2023–2060 and 2061–2100, respectively as compared to the observed time period 1982–2020. Furthermore, average monsoon rainfall in the catchment will decrease by 30–35% during 2023–2060 and 23–30% during 2061–2100 with respect to 1982–2020.

We propose an innovative methodology to estimate the formative discharge of alluvial rivers from remote sensing images. This procedure involves automatic extraction of the width of a channel from Landsat Thematic Mapper, Landsat 8, and Sentinel-1 satellite images. We translate the channel width extracted from satellite images to discharge using a width–discharge regime curve established previously by us for the Himalayan rivers. This regime curve is based on the threshold theory, a simple physical force balance that explains the first-order geometry of alluvial channels. Using this procedure, we estimate the formative discharge of six major rivers of the Himalayan foreland: the Brahmaputra, Chenab, Ganga, Indus, Kosi, and Teesta rivers. Except highly regulated rivers (Indus and Chenab), our estimates of the discharge from satellite images can be compared with the mean annual discharge obtained from historical records of gauging stations. We have shown that this procedure applies both to braided and single-thread rivers over a large territory. Furthermore, our methodology to estimate discharge from remote sensing images does not rely on continuous ground calibration.